Upload 7 files

commit inubit files

.gitattributes

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 virtimo-inubit/storage/vector_store.json filter=lfs diff=lfs merge=lfs -text
+storage/vector_store.json filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,86 @@
+import streamlit as st
+from llama_index import (
+    VectorStoreIndex,
+    SummaryIndex,
+    ServiceContext,
+    StorageContext,
+    Document,
+    load_index_from_storage, ChatPromptTemplate,
+)
+from llama_index.embeddings.openai import OpenAIEmbedding
+from llama_index import SimpleDirectoryReader
+from llama_index.llms import OpenAI, ChatMessage, MessageRole
+import gradio as gr
+from typing import List, cast, Optional
+
+# Function to load the dataframe and chat with it
+def chat_with_dataframe(question):
+    storage_context = StorageContext.from_defaults(
+        persist_dir=str("storage")
+    )
+
+    vector_index = cast(VectorStoreIndex, load_index_from_storage(storage_context))
+    vector_query_engine = vector_index.as_query_engine(
+        similarity_top_k=2
+    )
+    response = vector_query_engine.query(question)
+    return response.response
+
+
+# Streamlit interface
+def main():
+    st.sidebar.title('Virtimo - INUBIT')
+    st.sidebar.subheader('Ask me anything about the product!')
+    # File uploader in the sidebar
+
+    # st.title('Chat with Excel')
+    # Store LLM generated responses
+    if "messages" not in st.session_state.keys():
+        st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]
+
+    # Display or clear chat messages
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.write(message["content"])
+
+    # # Initialize conversation in session state if it doesn't exist
+    # if 'conversation' not in st.session_state:
+    #     st.session_state['conversation'] = []
+    #
+    # # Initialize user input in session state if it doesn't exist
+    # if 'user_input' not in st.session_state:
+    #     st.session_state['user_input'] = ''
+
+    # Chat interface similar to the provided image
+    # if st.session_state.uploaded_file:
+
+    # User-provided prompt
+    if prompt := st.chat_input():
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        with st.chat_message("user"):
+            st.write(prompt)
+
+        # Generate a new response if last message is not from assistant
+    if st.session_state.messages[-1]["role"] != "assistant":
+        with st.chat_message("assistant"):
+            with st.spinner("Thinking..."):
+                response = chat_with_dataframe(prompt)
+                placeholder = st.empty()
+                # full_response = ''
+                if response is not None:
+                    st.write(response)
+                    message = {"role": "assistant", "content": response}
+                    st.session_state.messages.append(message)
+                    # # Check if the response is iterable
+                    # if isinstance(response, (list, tuple)):
+                    #     full_response = ''.join(response)
+                    # elif isinstance(response, str):
+                    #     full_response = response
+                    # else:
+                    #     full_response = str(response)  # Convert non-string,
+                    # # placeholder.markdown(full_response)
+
+
+# Run the main function
+if __name__ == '__main__':
+    main()

requirements.txt

@@ -0,0 +1,3 @@
+streamlit==1.28.2
+llama-index=0.9.9
+

storage/graph_store.json

@@ -0,0 +1 @@
+{"graph_dict": {}}

storage/vector_store.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:989b7beb871da7b3843b5c774d4764ba3bd62a771528297f7c98ff5433fa95db
+size 56444037